ArticleBioinformatics (Oxford, England)2021
Multi-omics data integration by generative adversarial network.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- AI-bioinformatics synergy for precision medicine in brain diseases: integrating radiomics and multi-omics data.Briefings in bioinformatics · 2026Review
- Consistency of feature attribution in deep learning architectures for multi-omics.Scientific reports · 2026Article
- A powerful representation learning method for enhanced analysis of incomplete multi-omics data.NPJ systems biology and applications · 2026Article
- Graph designs for deep learning-based multi-omics integration.Briefings in bioinformatics · 2026Review
- Coherent cross-modal generation of synthetic biomedical data to advance multimodal precision medicine.PLoS computational biology · 2026Article
- Deep Learning-Enabled Multi-Omics Integration: A New Frontier in Precise Drug Target Discovery.Biology · 2026Review
- X-intNMF: a cross- and intra-omics regularized NMF framework for multi-omics integration.Bioinformatics (Oxford, England) · 2026Article
- AI-driven discovery in protein science for immunology and infectious disease research.Frontiers in bioinformatics · 2026Review
- Recent advances in omics-based research of mitral valve disease.Frontiers in cardiovascular medicine · 2026Review
- Computational Methods for Pathway and Network Analysis with MicroRNAs.Methods in molecular biology (Clifton, N.J.) · 2026Review
- SynOmics: integrating multi-omics data through feature interaction networks.Briefings in bioinformatics · 2025Article
- MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery.Journal of applied genetics · 2025Review
- Multi-omics prognostic marker discovery and survival modelling: a case study on multi-cancer survival analysis of women's specific tumours.Scientific reports · 2025Article
- Cancer survival prediction based on soft-label guided contrastive learning and global feature fusion.Bioinformatics (Oxford, England) · 2025Article
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- JASMINE: A powerful representation learning method for enhanced analysis of incomplete multi-omics data.bioRxiv : the preprint server for biology · 2025Article
- Global trends in machine learning applications for single-cell transcriptomics research.Hereditas · 2025Article
- A technical review of multi-omics data integration methods: from classical statistical to deep generative approaches.Briefings in bioinformatics · 2025Review
- HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Multimodal data integration in early-stage breast cancer.Breast (Edinburgh, Scotland) · 2025Review
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5 authors.
Funding
Abstract
motivationAccurate disease phenotype prediction plays an important role in the treatment of heterogeneous diseases like cancer in the era of precision medicine. With the advent of high throughput technologies, more comprehensive multi-omics data is now available that can effectively link the genotype to phenotype. However, the interactive relation of multi-omics datasets makes it particularly challenging to incorporate different biological layers to discover the coherent biological signatures and predict phenotypic outcomes. In this study, we introduce omicsGAN, a generative adversarial network model to integrate two omics data and their interaction network. The model captures information from the interaction network as well as the two omics datasets and fuse them to generate synthetic data with better predictive signals.
resultsLarge-scale experiments on The Cancer Genome Atlas breast cancer, lung cancer and ovarian cancer datasets validate that (i) the model can effectively integrate two omics data (e.g. mRNA and microRNA expression data) and their interaction network (e.g. microRNA-mRNA interaction network). The synthetic omics data generated by the proposed model has a better performance on cancer outcome classification and patients survival prediction compared to original omics datasets. (ii) The integrity of the interaction network plays a vital role in the generation of synthetic data with higher predictive quality. Using a random interaction network does not allow the framework to learn meaningful information from the omics datasets; therefore, results in synthetic data with weaker predictive signals. AVAILABILITY AND IMPLEMENTATION: Source code is available at: https://github.com/CompbioLabUCF/omicsGAN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.